# Dynamic pricing with Bayesian updates from online reviews

arXiv: https://arxiv.org/abs/2404.14953 · HTML: https://mlsanity.com/paper/2404.14953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Semi-parametric dynamic contextual pricing](https://mlsanity.com/paper/1901.02045/) (2019)

## Built on by (most cited first, 1 of 1)

- [DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization](https://mlsanity.com/paper/2512.03607/) (2025)

## Abstract (arXiv)

When launching new products, firms face uncertainty about market reception. Online reviews provide valuable information not only to consumers but also to firms, allowing firms to adjust the product characteristics, including its selling price. In this paper, we consider a pricing model with online reviews in which the quality of the product is uncertain, and both the seller and the buyers Bayesianly update their beliefs to make purchasing & pricing decisions. We model the seller's pricing problem as a basic bandits' problem and show a close connection with the celebrated Catalan numbers, allowing us to efficiently compute the overall future discounted reward of the seller. With this tool, we analyze and compare the optimal static and dynamic pricing strategies in terms of the probability of effectively learning the quality of the product.